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STOCHASTIC SIMULATION A NEW TOOL FOR ENGINEERING Gene Allen & Jacek Marczyk MSC.SoftwarePowerPoint Presentation

STOCHASTIC SIMULATION A NEW TOOL FOR ENGINEERING Gene Allen & Jacek Marczyk MSC.Software

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NDIA 6 th Annual Systems Engineering Supportability & Interoperability Conference STOCHASTIC SIMULATION A NEW TOOL FOR ENGINEERING Gene Allen & Jacek Marczyk MSC.Software October 22, 2003 PRESENTATION PURPOSE INTRODUCE NEW ENGINEERING METHOD ENABLED BY ADVANCES IN COMPUTERS

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### STOCHASTIC SIMULATION A NEW TOOL FOR ENGINEERINGGene Allen & Jacek MarczykMSC.Software

NDIA 6th Annual Systems Engineering

Supportability & Interoperability

Conference

October 22, 2003

- PRESENTATION PURPOSE
- INTRODUCE NEW ENGINEERING METHOD
- ENABLED BY ADVANCES IN COMPUTERS
- USES STOCHASTIC SIMULATION
- MODELS REFLECT REALITY IN TEST

- SHOW HOW METHOD IS BEING USED BY
- INDUSTRY
- REDUCES RISK AND COST
- IMPROVES RELIABILITY

Develop/Commercialize

manufacturing technologies

Director, Collaborative

Development, MSC & NCMS

Economic Development & Defense Procurement Assistant, Senator Byrd

U.S. Navy Nuclear Background

B.S. Nuclear Engineering, MIT

INTRODUCTION

- Dr. Jacek Marczyk
- Foremost practitioner of
- Stochastics
- Established & managed EU
- Promenvier Project at CASA
- Took Results to Auto Industry
- Applied Stochastics to crash
- Working next generation
- stochastic product

- PRESENTATION OUTLINE
- THE CHALLENGE
- STOCHASTICS PROCESS
- Uncertainty
- Monte Carlo Simulation
- Results (Meta Model)
- Design Improvement

- INDUSTRY APPLICATIONS
- IMPROVED ENGINEERING

DOMINATED BY ELIMINATING FAILURE MODES

Eliminate

Failure Modes

73%

COST

Single

Engine

Certification

Demonstration

10 %

Engineering

15 %

YEARS

Initial

Design

2 %

- Examples of Nonrecurring Development Costs
- Rocket Engines
- SSME $ 2.8 B
- F-1 $ 2.4 B
- J-2 $ 1.7 B

- Jet Engines
- F-100 $ 2.0 B

- Automobiles
- 1996 Ford Taurus $ 2.8 B

Vision

Eliminate

Failure Modes

73%

Demonstration

10%

Design & Engineering 70%

Test & Demonstration 30%

COST

Billions

COST

Certification

Certification

Engineering 15%

Initial

Design

2%

TIME

YEARS

TIME

Vision of 75% reduction in

Cost-Time profile to be realized

through use of computers

Historic Cost-Time profile

for aerospace/automotive platforms

THE PATH TO LOW COST DEVELOPMENT

THE NEEDED FUTURE

HISTORY

COST

COST

Certified

Product

Certified

Product

TIME

TIME

THIS VISION HAS NOT BEEN REALIZED

WHY? - LACK OF CONFIDENCE THAT MODELS CAN REPLACE TEST

WHY? - MODELS have been DETERMINISTIC while

REALITY IS STOCHASTIC

Gen Kern attended 10-06-03 SAE G-11 meeting in Detroit

- Relayed that the Army’s environment is probabilistic.
- Lack of reliability of Army platforms is costing taxpayers multi-billions of dollars.
- Equipment breakdowns have lead to soldier’s deaths in Iraq
- Model reliability versus test
- For systems fielded between 1985 and 1995
41% met their reliability targets during test.

- For systems fielded from 1996 to 2000
only 20% met their reliability targets during test.

- For systems fielded between 1985 and 1995

Incorporates Variability and Uncertainty

Based on Monte Carlo Simulation

Updated Latin Hypercube sampling

Independent of the Number of Variables

Generates a Meta Model

Does Not Violate Physics

No assumptions of continuity

“Not elegant, only gives the right answers.”

The Stochastic MethodExample of Physics Violation

This is NOT true

DEFINITION OF A STOCHASTIC PROBLEM

x1

x2

x3

y1

y2

Vibration

Buckling

Strength

Controls

….

Solution:

Establish tolerances for the

input and design variables.

Run a Monte Carlo simulation

in order to obtain the system’s

response in statistical terms.

Problem:

Given a set of uncertain

design/input variables,

determine the level of

uncertainty in the response

variables.

Loads

Boundary and initial conditions

Geometry errors

Assembly errors

Solver

Computer (round-off, truncation, etc.)

Engineer (choice of element type, algorithm,

mesh band-width, etc.)

Sources of UncertaintyStructural Material Scatter

MATERIAL CHARACTERISTIC CV

Metallic Rupture 8-15%

Buckling 14%

Carbon Fiber Rupture 10-17%

Screw, Rivet, Welding Rupture 8%

Bonding Adhesive strength 12-16%

Metal/metal 8-13%

Honeycomb Tension 16%

Shear, compression 10%

Face wrinkling 8%

Inserts Axial loading 12%

Thermal protection (AQ60) In-plane tension 12-24%

In-plane compression 15-20%

Load Scatter (aerospace)

LOAD TYPE ORIGIN OF RESULTS CV

Launch vehicle thrust STS, ARIANE 5%

Launch vehicle quasi-static loads STS, ARIANE, DELTA 30%

- POGO oscillation

- stages cut-off

- wind shear and gust

- landing (STS)

Transient ARIANE 4 60%

Thermal Thermal tests 8-20%

Deployment shocks (Solar array) Aerospatiale 10%

Thruster burn Calibration tests 2%

Acoustic ARIANE 4 and STS (flight) 30%

Vibration Satellite tests 20%

The Deception of Precise Geometry

Geometry imperfections may be described via stochastic fields.

Thickness

Density

Geometry

The Concept of a Meta-Model

Collection

of computer

runs =

Simulation

(CAE tomorrow)

Single

computer

run =

Analysis

(CAE today)

Understanding the physics of a phenomenon is equivalent to the

understanding of the topology and structure of these clouds.

Example of Meta-Model (13D)

7 inputs and 6

Outputs. The

meta-model is

result of a scan

with uniform

distributions.

REDUCE the Multi-Dimensional Cloud to

EASILY UNDERSTOOD INFORMATION

CLOUD:

POSITION provides information on PERFORMANCE

SCATTER represents QUALITY

SHAPE represents ROBUSTNESS

CORRELATION

Expresses the STRENGTH OF THE RELATIONSHIP Between Variables

Understanding the Meta ModelCORRELATION - A CONCEPT THAT SUPERSEDES SENSITIVITY

CORRELATION BETWEEN TWO VARIABLES

SHOWS THE STRENGTH BETWEEN VARIABLES

TAKES SCATTER IN ALL OTHER VARIABLES INTO ACCOUNT.

CORRELATION BETWEEN ANY PAIR OF VARIABLES CAN BE COMPUTED

INPUT - OUTPUT

OUTPUT - OUTPUT

INPUT IS A DESIGN OR NOISE VARIABLE

OUTPUT IS A PERFORMANCE, LIKE STRESS OR FREQUENCY

KNOWLEDGE OF THE CORRELATIONS IN A SYSTEM LEADS TO UNDERSTANDING HOW THE SYSTEM WORKS

Correlation The Decision Map

The decision map reflects how all system attributes react to

small simultaneous changes in all of the input variables.

Variable Ranking (Spearman)

Spearman variable ranking allows to determine where the engineering

effort must be concentrated and where tolerances may be relaxed.

First World-wide Stochastic Crash(BMW-CASA, August 1997)

- Stochastic material properties,
- thicknesses and stiffnesses
- (70 variables),initial and boundary
- conditions (angle, velocity and offset).
- 128 Monte Carlo samples on
- Cray T3E/512 (Stuttgart Univ.)
- 1 week-end of execution time.

Stochastic Design Improvement

1

2

Target location of meta-model

(mean of tests)

Improved

meta-model

3

4

Stochastic Design Improvement

40% offsetrigid wall

US-NCAP

Courtesy of BMW AG

Problem: Reduce weight by 15 kg without reducing performance

-0.15

-0.05

0.05

0.15

0.25

StochasticDesign ImprovementInitial design

Deformations (mm) Mass (kg)

12, 20, 47, 88, 103, 4, 9, 39, 82 184.6

Final design (Improved, not Optimal!)

Deformations (mm) Mass (kg)

17, 23, 49, 87, 108, 6, 10, 46, 86 169.3

Courtesy of BMW AG

This analysis took 90 executions of 200 hrs each. 33 lbs of saving per car is equivalent to $33. In 5 years, this means $36 M. The job can be run in 3 days on 256 CPUs.

Stochastic Design Improvement

Problem: reduce mass, maintain safety and

stiffness

Result:

16 kg mass reduction

20% reduction of A-pillar deformation

40% reduction of dashboard deformation

Cost = 60 runs (tolerances in all materials and

thicknesses) of PAM-Crash and MSC.Nastran

Courtesy, Nissan Motor Company

Stochastic Design Improvement

Problem: reduce mass, maintain safety and

stiffness

Result:

10 kg mass reduction

Cost = 85 runs of PAM-Crash and MSC.Nastran

Courtesy, UTS

Automotive Investment in Stochastic Crash Simulation

- Have Continued to INVEST since 1997
- Have bought High Performance Computing Clusters for Stochastic Car Crash Simulation

- Present level of Central Processing Units (CPU)
- dedicated to stochastic simulation (by company):

- BMW – 300
- Audi – 256
- Toyota – 300

- Jaguar – 48
- Mercedes – 384
- Nissan – 128

Evidence of Buy-in / Cost Savings Realized

Automotive Design Improvements from Stochastic Crash Simulation

MASS REDUCTION RESULTS with SAME OR BETTER CRASH PERFORMANCE

- Car Model 1 – 55 lb/car --- saved > $55 Million
- Car Model 2 – 35 lb --- > $35 Million
- Car Model 3 – 40 lb --- > $40 Million
- Car Model 4 – 33 lb --- > $33 Million
- Car Model 5 – 13 lb --- > $13 Million
- 1 lb mass reduction yields $1 per car
- Given 1 million cars made per model

Evidence of Buy-in / Cost Savings Realized

Satellite dispenser

Courtesy EADS-CASA

INITIAL CONFIGURATION

TUNED CONFIGURATION

TUNED CONFIGURATION

(+15,+45,-45,-15)

(+15,+45,-45,-15)

(0,+15,+45,-45,-15)

(0,+15,+45,-45,-15)

(0,+15,+45,-45,-15)x2

(0,+15,+45,-45,-15)x2

(+15,+45,-45,-15) (0,+15,-15,0)

(+15,+45,-45,-15) (0,+15,-15,0)

(0,+15,+45,-45,-15)x4

(0,+15,+45,-45,-15)x4

(+15,+45,-45,-15) (0,+15,-15,0)3

(+15,+45,-45,-15) (0,+15,-15,0)3

(0,+15,+45,-45,-15)x6

(0,+15,+45,-45,-15)x6

(+15,+45,-45,-15) (0,+15,-15,0)5

(+15,+45,-45,-15) (0,+15,-15,0)5

(+15,+45,-45,-15) (0,+15,-15,0)3

(0,+45,-45,0)4

(+15,+45,-45,-15) (0,+15,-15,0)3

(0,+45,-45,0)4

(0,+15,+45,-45,-15)x10

(0,+15,+45,-45,-15)x10

(+15,+45,-45,-15) (0,+15,-15,0)3

(0,+45,-45,0)6

(+15,+45,-45,-15) (0,+15,-15,0)3

(0,+45,-45,0)6

(0,+15,+45,-45,-15)x12

(0,+15,+45,-45,-15)x12

(03,+153,+302,+452,+602,+75,

-75,-602,-452,-302,-153,03)x2

(03,+153,+302,+452,+602,+75,

-75,-602,-452,-302,-153,03)x2

(06,+153,+303,+452,+602,+753,

-753,-602,-452,-302,-153)x2

(06,+153,+303,+452,+602,+753,

-753,-602,-452,-302,-153)x2

Mass= 436 kg

f1= 9.7 Hz

(200 kg are metallic parts

Not active in SDI)

Mass= 436 kg

f1= 9.7 Hz

Mass= 362 kg

f1= 9.47 Hz

Reliability > 0.999

Mass= 362kg

f1= 9.47 Hz

Satellite dispenserCourtesy EADS-CASA

Second order RS

First order RS

Optimum?

Different theories can be shown to fit the same set of observed data. The more complex a theory, the more credible it appears!

When the most common forms of uncertainty are incorporated, many optimization techniques don’t work. Therefore, surrogate models are used, which are not very realistic (therefore not very predictive!)

Improved EngineeringReality versus SurrogatesImproved Engineering many optimization techniques don’t work. Therefore, surrogate models are used, which are not very realistic (therefore not very predictive!)Remedies against risk

- Don’t optimise (leads to fragile designs)
- Design for robustness instead
- Design for less complexity (possible via proprietary methodologies)
- Search for potential pathologies
- Incorporate uncertainty into models –deterministic models by definition induce unjustified optimism
- Understand how (complex) systems really work – compute knowledge!

- Conclusions many optimization techniques don’t work. Therefore, surrogate models are used, which are not very realistic (therefore not very predictive!)
- Stochastic Simulation Reduces the Complexity in
- Modeling Reality
- Addresses Uncertainty and Variation
- Establishes credibility in modeling & simulation

- Easy to use
- Focuses on Robustness vice Optimization
- No assumptions of continuity
- Takes all inputs into account vice needing initial
- assumptions

- Reduces risk through better engineering
- Changing the general engineering process

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